MLS-C01 Exploratory Data Analysis Practice Test 20 — 15 Questions

Question 1

A data scientist is exploring a dataset and finds that the correlation between two features is 0.95. What should the data scientist do to address multicollinearity before training a linear regression model?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Remove one of the two features. A correlation of 0.95 between two features indicates severe multicollinearity, which inflates the variance of linear regression coefficients and makes them unstable and hard to interpret. The most direct and standard remedy is to remove one of the two highly correlated features, eliminating the redundancy. This preserves model interpretability and avoids the coefficient instability that multicollinearity causes.

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Apply L2 regularization

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Standardize the features

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Apply Principal Component Analysis

Question 2

A data scientist is performing EDA on a dataset containing text reviews. To understand the most common words, the data scientist generates a word cloud. Which preprocessing step is most important to ensure the word cloud reflects meaningful content?

Accepted Answer

Stop word removal. Removing stop words (common words like 'the', 'and') ensures that the word cloud highlights meaningful content. Stemming (C) may not be necessary for a word cloud. Tokenization (D) is fundamental but not the most critical for meaningfulness. POS tagging (B) is overkill.

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Part-of-speech tagging

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Stemming

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Tokenization

Question 3

A data analyst is exploring a dataset and notices that the target variable has a Poisson distribution. Which type of model is most appropriate for this target?

Accepted Answer

Poisson regression. Poisson regression is the correct choice because it is specifically designed for modeling count data where the target variable follows a Poisson distribution, which is characterized by non-negative integer values and a variance equal to the mean. This aligns directly with the data analyst's observation of a Poisson-distributed target, making Poisson regression the most appropriate generalized linear model (GLM) for this scenario.

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Linear regression

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Cox proportional hazards model

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Logistic regression

Question 4

Which TWO techniques are appropriate for detecting outliers in a univariate numeric dataset?

Accepted Answer

Z-score method. Options C and D are correct. The Z-score method identifies outliers by measuring how many standard deviations a data point is from the mean; points with |Z| > 3 are often considered outliers. The Interquartile Range (IQR) method defines outliers as points falling below Q1 - 1.5*IQR or above Q3 + 1.5*IQR. Option A (Cook's distance) is used in regression to identify influential points, not general univariate outlier detection. Option B (Mahalanobis distance) is a multivariate distance measure. Option E (DBSCAN) is a clustering algorithm that can identify outliers in multivariate space, but not specifically for univariate numeric data.

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Cook's distance

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Mahalanobis distance

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DBSCAN clustering

Question 5

Which TWO of the following are benefits of feature scaling for machine learning algorithms?

Accepted Answer

Faster convergence of gradient descent. Feature scaling, typically via standardization (z-score) or min-max normalization, ensures that gradient descent converges faster. Without scaling, features with larger numerical ranges dominate the gradient updates, causing the algorithm to oscillate and require more iterations to reach the optimum. Scaling produces a more spherical contour of the loss function, allowing gradient descent to take more direct steps toward the minimum.

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Eliminates the effect of outliers

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Reduces the need for feature selection

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Improves performance of decision tree algorithms

Question 6

A data scientist is analyzing a dataset with missing values in several columns. The dataset contains both numerical and categorical features. Which approach should the data scientist use to handle missing values while minimizing bias and preserving relationships in the data?

Accepted Answer

Use multiple imputation (e.g., MICE) to impute missing values. Multiple imputation (MICE) generates several plausible values for each missing entry by modeling each feature as a function of the others, then pools results across imputations. This preserves the covariance structure and relationships among numerical and categorical variables, and it reflects the uncertainty of the missing data rather than pretending a single value is exact. It is the recommended approach when the goal is minimizing bias in downstream analysis or modeling.

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Use forward-fill to propagate the last observed value

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Delete all rows with missing values

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Replace missing values with the mean or median of each column

Question 7

A data engineer is preparing a dataset for training a binary classification model. The target variable is highly imbalanced (95% negative, 5% positive). The engineer needs to split the data into training and test sets while maintaining the class distribution in both sets. Which method should the engineer use?

Accepted Answer

Use stratified random sampling to split the data. Stratified random sampling ensures the proportion of classes is preserved in both training and test sets. Option A is wrong because k-fold cross-validation is a model evaluation technique, not a method for splitting data into training and test sets; using it before splitting would not guarantee class balance. Option B is wrong because oversampling should be done after splitting to avoid data leakage and ensure the test set reflects the original distribution. Option C is wrong because a simple random 80/20 split may not preserve the class distribution due to random variation, especially with imbalanced data.

Answer

Use k-fold cross-validation and then split the data

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Oversample the minority class first, then do a random split

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Perform a simple random 80/20 split

Question 8

A data scientist is performing exploratory data analysis on a high-dimensional dataset with 500 features. The scientist wants to visualize the data in 2D to check for clusters. Which dimensionality reduction technique should the scientist use that preserves global structure and is computationally efficient for large datasets?

Accepted Answer

PCA. PCA is a linear dimensionality reduction technique that preserves global structure (variance) and is computationally efficient for large datasets. Option A is wrong because t-SNE is non-linear, slower, and focuses on preserving local structure, not global. Option B is wrong because LDA is a supervised technique that requires class labels, and it is not typically used for unsupervised exploration of clusters. Option D is wrong because UMAP is non-linear and can be slower than PCA; while it preserves both local and global structure to some extent, it is not as computationally efficient as PCA for very large datasets.

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t-SNE

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Linear Discriminant Analysis (LDA)

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UMAP

Question 9

A data analyst is examining a scatter plot of two variables and notices a strong positive correlation. Which of the following is a valid conclusion?

Accepted Answer

The two variables are related, but causation cannot be inferred. A strong positive correlation indicates that the two variables are related, but correlation alone does not imply causation. Option A is incorrect because correlation does not necessarily imply a linear relationship; it could be non-linear or monotonic. Option B is incorrect because correlation does not imply causation. Option D is incorrect because correlation does not guarantee accurate prediction; prediction requires a well-fitted model and additional validation.

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The relationship is linear

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One variable causes the other

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The relationship can be used to accurately predict one variable from the other

Question 10

A data scientist is working with a dataset containing 10,000 observations and 100 features. The scientist wants to detect outliers in the dataset. Which method is most appropriate for outlier detection in a high-dimensional space?

Accepted Answer

Use Isolation Forest. Isolation Forest is an ensemble method specifically designed for anomaly detection in high-dimensional datasets. It isolates observations by randomly partitioning the data; outliers require fewer splits to isolate, so they have shorter path lengths in the trees. This makes it robust to the curse of dimensionality and does not assume a particular data distribution, unlike distance- or density-based methods.

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Use Z-score to identify points beyond 3 standard deviations

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Use Mahalanobis distance

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Use interquartile range (IQR) for each feature

Question 11

A data engineer is building a data pipeline that aggregates customer transaction data. The engineer notices that some transactions have duplicate entries due to a system error. Which approach should the engineer use to identify and remove duplicates based on a unique transaction ID?

Accepted Answer

Use the drop_duplicates method on the transaction ID column. Using drop_duplicates on the transaction ID column is a straightforward and efficient method to remove duplicate rows based on the unique identifier. Option A is incorrect; sorting and checking consecutive rows is a valid but more complex approach, and not as direct as drop_duplicates. Option B is incorrect because fuzzy matching is designed for approximate matches, not exact duplicates. Option C is incorrect because grouping by all columns and summing would aggregate data, potentially losing information, and does not specifically remove duplicate transaction IDs.

Answer

Sort the data by transaction ID and then check consecutive rows for equality

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Use fuzzy matching to find similar transaction IDs

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Group by all columns and aggregate with sum

Question 12

A data analyst wants to understand the distribution of a continuous variable. Which visualization is most appropriate for this purpose?

Accepted Answer

Histogram. (Histogram) is correct because a histogram displays the frequency distribution of a continuous variable by grouping data into bins. Option A (Box plot) is incorrect because it shows summary statistics (median, quartiles, outliers) but not the full distribution. Option B (Bar chart) is incorrect because bar charts are for categorical data, not continuous. Option D (Scatter plot) is incorrect because it shows the relationship between two continuous variables, not the distribution of a single variable.

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Box plot

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Bar chart

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Scatter plot

Question 13

A data scientist is analyzing a dataset and finds that two features have a Pearson correlation coefficient of 0.95. Which TWO actions should the data scientist consider? (Choose two.)

Accepted Answer

Combine the two features into a single feature using PCA or averaging. A Pearson correlation coefficient of 0.95 indicates strong multicollinearity between the two features. Multicollinearity can inflate coefficient variances and reduce model interpretability. Two standard remedies are to remove one of the correlated features (Option D) or to combine them into a single feature using techniques like PCA, averaging, or summing (Option A). Option B (adding interaction terms) would introduce additional correlated terms and exacerbate multicollinearity. Option C (increasing regularization) can help stabilize coefficients but does not directly address the high pairwise correlation; it is often used as a secondary technique after feature selection or combination. Option E (standard scaling) does not change the correlation coefficient and therefore does not mitigate multicollinearity.

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Add interaction terms between the features

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Increase regularization strength in the model

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Apply standard scaling to both features

Question 14

A data science team at a financial services company is building a fraud detection model using a dataset of credit card transactions. The dataset contains 10 million rows and 20 features, including transaction amount, merchant category, time since last transaction, and customer ID. The target variable 'is_fraud' is highly imbalanced: only 0.1% of transactions are fraudulent. The team is performing exploratory data analysis (EDA) on a sample of 100,000 rows. They compute the correlation matrix and find that 'transaction amount' has a correlation of 0.02 with 'is_fraud'. They also plot the distribution of 'transaction amount' and see that it is heavily right-skewed with a long tail. The team wants to understand the relationship between 'transaction amount' and fraud more deeply before feature engineering. They have access to AWS SageMaker and can run processing jobs. Which course of action is most appropriate?

Accepted Answer

Create bins for 'transaction amount' (e.g., 0-10, 10-50, 50-100, 100+) and compute the fraud rate per bin to detect any non-linear patterns. Creating bins for 'transaction amount' and computing the fraud rate per bin is the most appropriate action because it can reveal non-linear relationships that correlation might miss. Given the heavy right-skew and the near-zero correlation, binning allows the team to see if fraud rates vary across different ranges of transaction amounts, which could inform feature engineering.

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Conclude that 'transaction amount' is not predictive because the correlation is near zero

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Train a random forest model on the sample and use feature importance to assess the predictive power of 'transaction amount'

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Apply a log transformation to 'transaction amount' to reduce skewness and re-run the correlation analysis

Question 15

A data scientist is performing exploratory data analysis on a dataset with missing values. They want to understand the distribution of each feature and identify outliers. Which AWS service can be used to create visualizations such as histograms and box plots without writing any code?

Accepted Answer

Amazon QuickSight. Amazon QuickSight is a serverless, machine learning-powered business intelligence service that allows users to create interactive dashboards and visualizations without writing code. Option A is wrong because Amazon EMR is a big data platform, not primarily for visualization. Option B is wrong because AWS Glue is used for ETL, not visualization. Option C is correct. Option D is wrong because Amazon SageMaker Studio requires coding for custom visualizations. Option E is wrong because Amazon Athena is a query service, not a visualization tool.

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Amazon EMR

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AWS Glue

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Amazon SageMaker Studio

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Amazon Athena